Attribute and rating co-extraction

Abstract

Embodiments of the present disclosure relate to attribute and rating co-extraction. According to embodiments of the present disclosure, a method is proposed. The method comprises: determining, by a first sub-network of a model, a first feature representation based on a first token contained in a text, the first feature representation indicating semantic information of the first token in the text; determining, by a second sub-network of the model, first attribute information associated with the first token based on the first feature representation, the first attribute information indicating a first attribute involved in the text; and determining, by a third sub-network of the model, first rating information associated with the first token based on the first feature representation, the first rating information indicating a rating related to the first attribute.

I/We claim: 
     
         1 . A method, comprising:
 determining, by a first sub-network of a model, a first feature representation based on a first token contained in a text, the first feature representation indicating semantic information of the first token in the text;   determining, by a second sub-network of the model, first attribute information associated with the first token based on the first feature representation, the first attribute information indicating a first attribute involved in the text; and   determining, by a third sub-network of the model, first rating information associated with the first token based on the first feature representation, the first rating information indicating a rating related to the first attribute.   
     
     
         2 . The method of  claim 1 , wherein the text further comprises a second token following the first token,
 determining the first feature representation comprises:
 obtaining first status information associated with the first token, the first status information indicating a status of the first sub-network; and 
   the method further comprises:
 determining, by the first sub-network, a second feature representation based on the second token and the first status information, the second feature representation indicating the semantic information of the second token in the text; 
 determining, by the second sub-network, a second attribute information associated with the second token based on the second feature representation, the second attribute information indicating a second attribute involved in the text; and 
 determining, by the third sub-network, a second rating information associated with the second token based on the second feature representation, the second rating information indicating a rating related to the second attribute. 
   
     
     
         3 . The method of  claim 2 , wherein the second token corresponds to a last token in the text, the model further comprises a fourth sub-network,
 determining the second feature representation comprises:
 obtaining second status information associated with the second token, the second status information indicating a status of the first sub-network; and 
   the method further comprises:
 determining, by the fourth sub-network, domain information associated with the text, the domain information indicating a domain involved by the text. 
   
     
     
         4 . The method of  claim 2 , further comprising:
 determining target attribute information associated with the text based on the first attribute information and the second attribute information, the target attribute information indicating a set of attributes involved in the text; and   determining target rating information associated with the text based on the first rating information and the second rating information, the target rating information indicating a respective rating of each of the set of attributes.   
     
     
         5 . The method of  claim 1 , wherein determining the first feature representation comprises:
 determining an embedding of the first token; and   determining the first feature representation based on the embedding.   
     
     
         6 . The method of  claim 5 , wherein the embedding is pre-trained by a language model. 
     
     
         7 . The method of  claim 1 , wherein the first sub-network comprises a long short-term memory (LSTM) unit or a bidirectional encoder representations from transformers (BERT) unit. 
     
     
         8 . A system, comprising:
 at least one processor; and   at least one memory communicatively coupled to the at least one processor and comprising computer-readable instructions that upon execution by the at least one processor cause the at least one processor to perform actions comprising:
 determining, by a first sub-network of a model, a first feature representation based on a first token contained in a text, the first feature representation indicating semantic information of the first token in the text; 
 determining, by a second sub-network of the model, first attribute information associated with the first token based on the first feature representation, the first attribute information indicating a first attribute involved in the text; and 
 determining, by a third sub-network of the model, first rating information associated with the first token based on the first feature representation, the first rating information indicating a rating related to the first attribute. 
   
     
     
         9 . The system of  claim 8 , wherein the text further comprises a second token following the first token,
 determining the first feature representation comprises:
 obtaining first status information associated with the first token, the first status information indicating a status of the first sub-network; and 
   the actions further comprises:
 determining, by the first sub-network, a second feature representation based on the second token and the first status information, the second feature representation indicating the semantic information of the second token in the text; 
 determining, by the second sub-network, a second attribute information associated with the second token based on the second feature representation, the second attribute information indicating a second attribute involved in the text; and 
 determining, by the third sub-network, a second rating information associated with the second token based on the second feature representation, the second rating information indicating a rating related to the second attribute. 
   
     
     
         10 . The method of  claim 9 , wherein the second token corresponds to a last token in the text, the model further comprises a fourth sub-network,
 determining the second feature representation comprises:
 obtaining second status information associated with the second token, the second status information indicating a status of the first sub-network; and 
   the actions further comprises:
 determining, by the fourth sub-network, domain information associated with the text, the domain information indicating a domain involved by the text. 
   
     
     
         11 . The system of  claim 9 , wherein the actions further comprises:
 determining target attribute information associated with the text based on the first attribute information and the second attribute information, the target attribute information indicating a set of attributes involved in the text; and   determining target rating information associated with the text based on the first rating information and the second rating information, the target rating information indicating a respective rating of each of the set of attributes.   
     
     
         12 . The system of  claim 8 , wherein determining the first feature representation comprises:
 determining an embedding of the first token; and   determining the first feature representation based on the embedding.   
     
     
         13 . The system of  claim 12 , wherein the embedding is pre-trained by a language model. 
     
     
         14 . The system of  claim 8 , wherein the first sub-network comprises a long short-term memory (LSTM) unit or a bidirectional encoder representations from transformers (BERT) unit. 
     
     
         15 . A non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a computing device cause the computing device to perform actions comprising:
 determining, by a first sub-network of a model, a first feature representation based on a first token contained in a text, the first feature representation indicating semantic information of the first token in the text;   determining, by a second sub-network of the model, first attribute information associated with the first token based on the first feature representation, the first attribute information indicating a first attribute involved in the text; and   determining, by a third sub-network of the model, first rating information associated with the first token based on the first feature representation, the first rating information indicating a rating related to the first attribute.   
     
     
         16 . The non-transitory computer-readable storage medium of  claim 15 , wherein the text further comprises a second token following the first token,
 determining the first feature representation comprises:
 obtaining first status information associated with the first token, the first status information indicating a status of the first sub-network; and 
   the actions further comprises:
 determining, by the first sub-network, a second feature representation based on the second token and the first status information, the second feature representation indicating the semantic information of the second token in the text; 
 determining, by the second sub-network, a second attribute information associated with the second token based on the second feature representation, the second attribute information indicating a second attribute involved in the text; and 
 determining, by the third sub-network, a second rating information associated with the second token based on the second feature representation, the second rating information indicating a rating related to the second attribute. 
   
     
     
         17 . The non-transitory computer-readable storage medium of  claim 16 , wherein the second token corresponds to a last token in the text, the model further comprises a fourth sub-network,
 determining the second feature representation comprises:
 obtaining second status information associated with the second token, the second status information indicating a status of the first sub-network; and 
   the actions further comprises:
 determining, by the fourth sub-network, domain information associated with the text, the domain information indicating a domain involved by the text. 
   
     
     
         18 . The non-transitory computer-readable storage medium of  claim 17 , wherein the actions further comprises:
 determining target attribute information associated with the text based on the first attribute information and the second attribute information, the target attribute information indicating a set of attributes involved in the text; and   determining target rating information associated with the text based on the first rating information and the second rating information, the target rating information indicating a respective rating of each of the set of attributes.   
     
     
         19 . The non-transitory computer-readable storage medium of  claim 15 , wherein determining the first feature representation comprises:
 determining an embedding of the first token; and   determining the first feature representation based on the embedding.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 19 , wherein the embedding is pre-trained by a language model.